AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time
	1_Baseline	Baseline	rmse	224427	0.44
	2_Default_LightGBM	LightGBM	rmse	20231.9	10.17
	3_Default_Xgboost	Xgboost	rmse	18730.7	4.05
	4_Default_CatBoost	CatBoost	rmse	18662.4	23.92
	5_Default_RandomForest	Random Forest	rmse	30872.6	5.62
	6_Default_ExtraTrees	Extra Trees	rmse	29627.9	3.03
	16_LightGBM	LightGBM	rmse	18876.1	2.03
	7_Xgboost	Xgboost	rmse	19778.1	3.18
	25_CatBoost	CatBoost	rmse	18955.9	28.84
	34_RandomForest	Random Forest	rmse	28720	2.23
	43_ExtraTrees	Extra Trees	rmse	30706.8	3.62
	17_LightGBM	LightGBM	rmse	19692.4	2.05
	8_Xgboost	Xgboost	rmse	19865.9	3
	26_CatBoost	CatBoost	rmse	20977.1	30.11
	35_RandomForest	Random Forest	rmse	36339.9	3.29
	44_ExtraTrees	Extra Trees	rmse	34706.7	4.78
	18_LightGBM	LightGBM	rmse	19208	3.34
	9_Xgboost	Xgboost	rmse	18690.7	2.13
	27_CatBoost	CatBoost	rmse	19979.3	26.96
	36_RandomForest	Random Forest	rmse	26551.3	2.56
	45_ExtraTrees	Extra Trees	rmse	24831.2	5.59
	19_LightGBM	LightGBM	rmse	21039.4	4.85
	10_Xgboost	Xgboost	rmse	20136.7	2.23
	28_CatBoost	CatBoost	rmse	24195.6	7.93
	37_RandomForest	Random Forest	rmse	27180.4	7.37
	46_ExtraTrees	Extra Trees	rmse	25686.7	3.45
	20_LightGBM	LightGBM	rmse	20632.4	1.76
	11_Xgboost	Xgboost	rmse	19374.2	2.15
	29_CatBoost	CatBoost	rmse	20066.1	15.91
	38_RandomForest	Random Forest	rmse	23939.4	2.95
	47_ExtraTrees	Extra Trees	rmse	23606.9	5.2
	21_LightGBM	LightGBM	rmse	18923.2	3.65
	12_Xgboost	Xgboost	rmse	18492.3	2.11
	30_CatBoost	CatBoost	rmse	20094.8	11.78
	39_RandomForest	Random Forest	rmse	26576.7	5.19
	48_ExtraTrees	Extra Trees	rmse	23898.4	3.51
	22_LightGBM	LightGBM	rmse	19927.3	1.98
	13_Xgboost	Xgboost	rmse	18061.5	3.13
	31_CatBoost	CatBoost	rmse	19253.6	74.34
	40_RandomForest	Random Forest	rmse	30217.1	6.63
	49_ExtraTrees	Extra Trees	rmse	30895.6	3.95
	23_LightGBM	LightGBM	rmse	18698.5	3.42
	14_Xgboost	Xgboost	rmse	19439.3	2.12
	32_CatBoost	CatBoost	rmse	21205.8	21.69
	41_RandomForest	Random Forest	rmse	29723	3.33
	50_ExtraTrees	Extra Trees	rmse	30456.9	3.92
	24_LightGBM	LightGBM	rmse	18649.2	3.4
	15_Xgboost	Xgboost	rmse	19118.3	3.53
	33_CatBoost	CatBoost	rmse	17947.9	91.97
	42_RandomForest	Random Forest	rmse	21914.6	6.59
	51_ExtraTrees	Extra Trees	rmse	23076.9	10.8
	13_Xgboost_categorical_mix	Xgboost	rmse	18022.5	3.91
	33_CatBoost_GoldenFeatures	CatBoost	rmse	16944.9	70.69
	13_Xgboost_categorical_mix_GoldenFeatures	Xgboost	rmse	17101.9	5.04
	13_Xgboost_GoldenFeatures	Xgboost	rmse	17295.2	4.64
	33_CatBoost_KMeansFeatures	CatBoost	rmse	17341.9	139.25
	13_Xgboost_categorical_mix_KMeansFeatures	Xgboost	rmse	18067.3	6.51
	13_Xgboost_KMeansFeatures	Xgboost	rmse	18164	4.93
	33_CatBoost_GoldenFeatures_RandomFeature	CatBoost	rmse	16618.2	52.24
	33_CatBoost_GoldenFeatures_SelectedFeatures	CatBoost	rmse	16913.2	71.12
	13_Xgboost_categorical_mix_GoldenFeatures_SelectedFeatures	Xgboost	rmse	16692.8	5.82
	24_LightGBM_SelectedFeatures	LightGBM	rmse	19005.8	2.49
	42_RandomForest_SelectedFeatures	Random Forest	rmse	21880	4.98
	51_ExtraTrees_SelectedFeatures	Extra Trees	rmse	23159.3	4.22
	52_Xgboost_GoldenFeatures_SelectedFeatures	Xgboost	rmse	17398	5.91
	53_CatBoost_GoldenFeatures_SelectedFeatures	CatBoost	rmse	16791.3	57.34
	54_CatBoost_GoldenFeatures_SelectedFeatures	CatBoost	rmse	17326.2	107.31
	55_CatBoost_GoldenFeatures	CatBoost	rmse	16757.1	51.38
	56_CatBoost_GoldenFeatures	CatBoost	rmse	17372.1	96.87
	57_Xgboost_GoldenFeatures	Xgboost	rmse	17658.8	6.86
	58_Xgboost_GoldenFeatures	Xgboost	rmse	17357.4	7.35
	59_CatBoost	CatBoost	rmse	17468.5	43.68
	60_CatBoost	CatBoost	rmse	17698.4	180.05
	61_LightGBM	LightGBM	rmse	18649.2	3.59
	62_LightGBM	LightGBM	rmse	18698.5	3.57
	63_LightGBM	LightGBM	rmse	18698.5	3.56
	64_LightGBM	LightGBM	rmse	18876.1	2.41
	65_RandomForest_SelectedFeatures	Random Forest	rmse	22115.9	5.18
	66_RandomForest	Random Forest	rmse	21809.6	8.82
	67_ExtraTrees	Extra Trees	rmse	22542.3	4.73
	68_ExtraTrees_SelectedFeatures	Extra Trees	rmse	23128.7	2.66
	69_ExtraTrees	Extra Trees	rmse	23578.8	7.24
	70_ExtraTrees	Extra Trees	rmse	23482.8	4.62
	71_RandomForest	Random Forest	rmse	24538.4	4.96
	72_RandomForest	Random Forest	rmse	24949.1	3.22
	73_Xgboost_GoldenFeatures_SelectedFeatures	Xgboost	rmse	16842.5	5.13
	74_Xgboost_GoldenFeatures_SelectedFeatures	Xgboost	rmse	16984.1	6.16
	75_CatBoost_GoldenFeatures	CatBoost	rmse	16629.1	40.05
	76_CatBoost_GoldenFeatures_SelectedFeatures	CatBoost	rmse	16308.2	42.93
	77_Xgboost_GoldenFeatures	Xgboost	rmse	16901.7	5.92
	78_Xgboost_GoldenFeatures	Xgboost	rmse	17148.1	5.2
	79_Xgboost_GoldenFeatures	Xgboost	rmse	16560.7	5.33
	80_Xgboost_GoldenFeatures	Xgboost	rmse	17231	6.65
	81_LightGBM	LightGBM	rmse	18033.2	3
	82_LightGBM	LightGBM	rmse	18567.9	3.06
	83_LightGBM	LightGBM	rmse	18033.2	3.02
	84_LightGBM	LightGBM	rmse	18567.9	3.05
	85_LightGBM	LightGBM	rmse	19046	2.54
	86_RandomForest	Random Forest	rmse	21940.7	10.61
	87_ExtraTrees	Extra Trees	rmse	22604.4	5.25
	88_ExtraTrees_SelectedFeatures	Extra Trees	rmse	22535.7	4.79
the best	Ensemble	Ensemble	rmse	15441.6	15.69

Metric	Score
MAE	13750.6
MSE	4.05489e+08
RMSE	20136.7
R2	0.991949
MAPE	0.0499059

Metric	Score
MAE	13231.4
MSE	3.75361e+08
RMSE	19374.2
R2	0.992547
MAPE	0.0490204

Metric	Score
MAE	12750.6
MSE	3.41966e+08
RMSE	18492.3
R2	0.99321
MAPE	0.0465589

Metric	Score
MAE	12525.7
MSE	3.26218e+08
RMSE	18061.5
R2	0.993523
MAPE	0.0458903

Metric	Score
MAE	12297
MSE	3.24812e+08
RMSE	18022.5
R2	0.993551
MAPE	0.0444417

AutoML Leaderboard

AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 10_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

Metric details:

Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

Summary of 11_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

Metric details:

Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

Summary of 12_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

Metric details:

Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

Summary of 13_Xgboost

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

Metric details:

Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

Summary of 13_Xgboost_categorical_mix

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

Metric details:

Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

Summary of 13_Xgboost_categorical_mix_GoldenFeatures

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

Metric details:

Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

Summary of 13_Xgboost_categorical_mix_GoldenFeatures_SelectedFeatures

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time

Metric details:

Learning curves

Permutation-based Importance

True vs Predicted

Predicted vs Residuals

Summary of 13_Xgboost_categorical_mix_KMeansFeatures

Extreme Gradient Boosting (Xgboost)

Validation

Optimized metric

Training time